{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside `cs231n/classifiers/linear_svm.py`. \n",
    "\n",
    "As you can see, we have prefilled the function `svm_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.048553\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -5.265017 analytic: -5.265017, relative error: 6.354647e-11\n",
      "numerical: 18.276532 analytic: 18.276532, relative error: 6.944852e-12\n",
      "numerical: -14.450823 analytic: -14.450823, relative error: 7.252225e-12\n",
      "numerical: 6.022956 analytic: 6.022956, relative error: 4.010414e-11\n",
      "numerical: -5.800046 analytic: -5.800046, relative error: 2.166648e-11\n",
      "numerical: -1.652496 analytic: -1.652496, relative error: 1.712984e-10\n",
      "numerical: -1.590817 analytic: -1.590817, relative error: 1.152119e-10\n",
      "numerical: -33.663294 analytic: -33.663294, relative error: 3.802382e-12\n",
      "numerical: -17.734874 analytic: -17.734874, relative error: 1.802902e-11\n",
      "numerical: -7.289302 analytic: -7.289302, relative error: 2.031278e-11\n",
      "==========\n",
      "numerical: 2.847539 analytic: 2.847539, relative error: 1.473421e-10\n",
      "numerical: -2.355177 analytic: -2.355177, relative error: 4.556013e-11\n",
      "numerical: -9.696384 analytic: -9.696384, relative error: 2.752548e-12\n",
      "numerical: -34.925527 analytic: -34.925527, relative error: 5.014718e-12\n",
      "numerical: -29.228155 analytic: -29.228155, relative error: 1.442507e-12\n",
      "numerical: -0.988716 analytic: -0.988716, relative error: 2.841532e-10\n",
      "numerical: 4.372622 analytic: 4.372622, relative error: 7.338333e-11\n",
      "numerical: 6.521632 analytic: 6.521632, relative error: 1.267449e-11\n",
      "numerical: -15.572825 analytic: -15.572825, relative error: 4.381513e-12\n",
      "numerical: 15.648856 analytic: 15.648856, relative error: 1.360573e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "print(\"==========\")\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "id": "vectorized_time_1",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.048553e+00 computed in 0.160571s\n",
      "Vectorized loss: 9.048553e+00 computed in 0.003995s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "id": "vectorized_time_2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.134640s\n",
      "Vectorized loss and gradient: computed in 0.004991s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss. Your code for this part will be written inside `cs231n/classifiers/linear_classifier.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "id": "sgd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 21.889786\n",
      "iteration 100 / 1500: loss 14.194888\n",
      "iteration 200 / 1500: loss 12.438836\n",
      "iteration 300 / 1500: loss 9.855264\n",
      "iteration 400 / 1500: loss 9.240223\n",
      "iteration 500 / 1500: loss 9.206669\n",
      "iteration 600 / 1500: loss 9.065810\n",
      "iteration 700 / 1500: loss 9.190259\n",
      "iteration 800 / 1500: loss 8.785613\n",
      "iteration 900 / 1500: loss 8.361707\n",
      "iteration 1000 / 1500: loss 8.512915\n",
      "iteration 1100 / 1500: loss 7.982543\n",
      "iteration 1200 / 1500: loss 7.477927\n",
      "iteration 1300 / 1500: loss 7.561704\n",
      "iteration 1400 / 1500: loss 7.649515\n",
      "That took 11.559077s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=5e-8, reg=2.5e-6,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "id": "validate"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.366714\n",
      "validation accuracy: 0.366000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "id": "tuning",
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 1.000000e+04 train accuracy: 0.383388 val accuracy: 0.386000\n",
      "lr 1.000000e-07 reg 1.428571e+04 train accuracy: 0.381776 val accuracy: 0.408000\n",
      "lr 1.000000e-07 reg 1.857143e+04 train accuracy: 0.372347 val accuracy: 0.398000\n",
      "lr 1.000000e-07 reg 2.285714e+04 train accuracy: 0.368510 val accuracy: 0.368000\n",
      "lr 1.000000e-07 reg 2.714286e+04 train accuracy: 0.365082 val accuracy: 0.380000\n",
      "lr 1.000000e-07 reg 3.142857e+04 train accuracy: 0.370449 val accuracy: 0.369000\n",
      "lr 1.000000e-07 reg 3.571429e+04 train accuracy: 0.361673 val accuracy: 0.370000\n",
      "lr 1.000000e-07 reg 4.000000e+04 train accuracy: 0.359327 val accuracy: 0.350000\n",
      "lr 3.250000e-07 reg 1.000000e+04 train accuracy: 0.361857 val accuracy: 0.355000\n",
      "lr 3.250000e-07 reg 1.428571e+04 train accuracy: 0.360286 val accuracy: 0.366000\n",
      "lr 3.250000e-07 reg 1.857143e+04 train accuracy: 0.360388 val accuracy: 0.362000\n",
      "lr 3.250000e-07 reg 2.285714e+04 train accuracy: 0.363224 val accuracy: 0.390000\n",
      "lr 3.250000e-07 reg 2.714286e+04 train accuracy: 0.358367 val accuracy: 0.373000\n",
      "lr 3.250000e-07 reg 3.142857e+04 train accuracy: 0.349755 val accuracy: 0.346000\n",
      "lr 3.250000e-07 reg 3.571429e+04 train accuracy: 0.344102 val accuracy: 0.363000\n",
      "lr 3.250000e-07 reg 4.000000e+04 train accuracy: 0.350755 val accuracy: 0.369000\n",
      "lr 5.500000e-07 reg 1.000000e+04 train accuracy: 0.342449 val accuracy: 0.354000\n",
      "lr 5.500000e-07 reg 1.428571e+04 train accuracy: 0.339653 val accuracy: 0.353000\n",
      "lr 5.500000e-07 reg 1.857143e+04 train accuracy: 0.345469 val accuracy: 0.348000\n",
      "lr 5.500000e-07 reg 2.285714e+04 train accuracy: 0.344755 val accuracy: 0.373000\n",
      "lr 5.500000e-07 reg 2.714286e+04 train accuracy: 0.343327 val accuracy: 0.347000\n",
      "lr 5.500000e-07 reg 3.142857e+04 train accuracy: 0.323796 val accuracy: 0.323000\n",
      "lr 5.500000e-07 reg 3.571429e+04 train accuracy: 0.327939 val accuracy: 0.337000\n",
      "lr 5.500000e-07 reg 4.000000e+04 train accuracy: 0.316163 val accuracy: 0.329000\n",
      "lr 7.750000e-07 reg 1.000000e+04 train accuracy: 0.349837 val accuracy: 0.346000\n",
      "lr 7.750000e-07 reg 1.428571e+04 train accuracy: 0.329204 val accuracy: 0.338000\n",
      "lr 7.750000e-07 reg 1.857143e+04 train accuracy: 0.329449 val accuracy: 0.319000\n",
      "lr 7.750000e-07 reg 2.285714e+04 train accuracy: 0.288061 val accuracy: 0.301000\n",
      "lr 7.750000e-07 reg 2.714286e+04 train accuracy: 0.333878 val accuracy: 0.362000\n",
      "lr 7.750000e-07 reg 3.142857e+04 train accuracy: 0.290469 val accuracy: 0.287000\n",
      "lr 7.750000e-07 reg 3.571429e+04 train accuracy: 0.280102 val accuracy: 0.294000\n",
      "lr 7.750000e-07 reg 4.000000e+04 train accuracy: 0.304265 val accuracy: 0.314000\n",
      "lr 1.000000e-06 reg 1.000000e+04 train accuracy: 0.335102 val accuracy: 0.328000\n",
      "lr 1.000000e-06 reg 1.428571e+04 train accuracy: 0.277776 val accuracy: 0.291000\n",
      "lr 1.000000e-06 reg 1.857143e+04 train accuracy: 0.269673 val accuracy: 0.272000\n",
      "lr 1.000000e-06 reg 2.285714e+04 train accuracy: 0.303837 val accuracy: 0.313000\n",
      "lr 1.000000e-06 reg 2.714286e+04 train accuracy: 0.295204 val accuracy: 0.328000\n",
      "lr 1.000000e-06 reg 3.142857e+04 train accuracy: 0.267449 val accuracy: 0.272000\n",
      "lr 1.000000e-06 reg 3.571429e+04 train accuracy: 0.246429 val accuracy: 0.260000\n",
      "lr 1.000000e-06 reg 4.000000e+04 train accuracy: 0.294082 val accuracy: 0.290000\n",
      "best validation accuracy achieved during cross-validation: 0.408000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "# Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "\n",
    "# Provided as a reference. You may or may not want to change these hyperparameters\n",
    "\n",
    "learning_rates = [1e-7, 1e-6]\n",
    "regularization_strengths = [1e4, 4e4]\n",
    "\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# some notes I took for myself:\n",
    "# 1. first tested with small reg (=2.5e-6), I change lr interval from [1e-7, 1e-5] to [5e-8, 1e-6] cause 5e-6 dose not converge\n",
    "# 2. then observe the tendency relationship of `val_acc` and `reg` for any given `lr` to shrink interval of `reg`\n",
    "# 3. np.mean() can calculate accurancy in one short line !!!! not even using the list comprehension\n",
    "# 4. finally, remember to train model with a larger value for num_iters\n",
    "# 5. but why can he/she do so well ... orz: https://tomaxent.com/2017/03/03/cs231n-Assignment-1-svm/\n",
    "\n",
    "for lr in np.linspace(learning_rates[0], learning_rates[1], 5):\n",
    "    for reg in np.linspace(regularization_strengths[0], regularization_strengths[1], 8):\n",
    "\n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train, y_train, learning_rate=lr, reg=reg, num_iters=1500)\n",
    "        \n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_acc = np.mean(y_train_pred == y_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_acc = np.mean(y_val_pred == y_val)\n",
    "    \n",
    "        results[(lr, reg)] = (train_acc, val_acc)\n",
    "\n",
    "        print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "            lr, reg, train_acc, val_acc))\n",
    "        \n",
    "        if best_val < val_acc:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "        \n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "'''\n",
    "print(\"===============\")\n",
    "\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "'''\n",
    "\n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "import pdb\n",
    "\n",
    "# pdb.set_trace()\n",
    "\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.tight_layout(pad=3)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "id": "test"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.365000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "best_svm = LinearSVM()\n",
    "best_svm.train(X_train, y_train, learning_rate=1e-7, reg=2e4, num_iters=1500)\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ \n",
    "\n",
    "- 多分类SVM就像在获取训练集中不同类别的图片模板\n",
    "- 通过梯度公式可知，样本i对W中y[i]类（其中一行/一列向量）的梯度影响因子的系数是负数，且绝对值比W中非y[i]类的梯度影响因子的系数要大\n",
    "- 按照负梯度方向下降时，W中y[i]类会以比其他类更快的速度逼近X[i]\n",
    "- 所以W实际上是在学习（逼近）训练集中图片的像素值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
